医学
谵妄
阿卡克信息准则
康复
功能独立性测度
逻辑回归
统计的
物理疗法
观察研究
急诊医学
重症监护医学
内科学
统计
数学
作者
Marco G. Ceppi,Marlene Rauch,Julia Spöndlin,Christoph Meier,Peter S. Sándor
标识
DOI:10.1016/j.jamda.2023.07.003
摘要
ObjectivesTo develop a clinical model to predict the risk of an individual patient developing delirium during inpatient rehabilitation, based on patient characteristics and clinical data available on admission.DesignRetrospective observational study based on electronic health record data.Setting and ParticipantsWe studied a previously validated data set of inpatients including incident delirium episodes during rehabilitation. These patients were admitted to ZURZACH Care, Rehaklinik Bad Zurzach, a Swiss inpatient rehabilitation clinic, between January 1, 2015, and December 31, 2018.MethodsWe performed logistic regression analysis using backward and forward selection with alpha = 0.01 to remove any noninformative potential predictor. We subsequentially used the Akaike information criterion (AIC) to select the final model among the resulting “intermediate” models. Discrimination of the final prediction model was evaluated using the C-statistic.ResultsOf the 20 candidate predictor variables, 6 were included in the final prediction model: a linear spline of age with 1 knot at 60 years and a linear spline of the functional independence measure (FIM), a measure of the functional degree of patients independency, with 1 knot at 64 points, diagnosis of disorders of fluid, electrolyte, and acid-base balance (E87), use of other analgesic and antipyretics (N02B), use of anti-parkinson drugs (N04B), and an anticholinergic burden score (ACB) of ≥3 points.Conclusions and ImplicationsOur clinical prediction model could, upon validation, identify patients at risk of incident delirium at admission to inpatient rehabilitation, and thus enable targeted prevention strategies.
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